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IDL - International Digital Library Of Technology & Research Volume 1, Issue 5, May 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017

Effect of Data Size on Feature Set Using Classification in Health Domain Uttham H1*, Gowramma2 1 PG-Student, 2Associate Professor, Dept. Computer Science & Engineering, D.B.I.T, Banglore, Karnataka, India. 1* utthamhmanju@gmail.com,2gowramma@gmail.com.

ABSTRACT: In health domain, the major critical issue is prediction of disease in early stage. Prediction of disease is mainly based on the experience of physician so many machine learning approach contribute their work in the prediction of disease. In existing approaches, either prediction or feature selection has been concentrated. The aim of this paper is to present the effect of data size and set of features in the prediction of disease in health domain using NaĂŻve Bayes. This shows how each attribute or combination of attribute behaves on different size of dataset. Keywords: Machine Learning, Classification, NaĂŻve Bayes, feature selection. 1. INTRODUCTION In health, domain diagnosis of disease is

the experience. If the physician has more

very challenging task. Earlier prediction can

experience, then he may predict well. if the

made based on some lab test. Using this lab

physician has less experience then he may

test report the physician will decide whether

predict wrongly.to overcome from this

the patient has disease or not but prediction

problem

of disease by physician mainly depend on

approaches like KNN, SVM, ANN to

IDL - International Digital Library

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machine

learning

has

many

Copyright@IDL-2017


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